Triple
T37352627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 90 Day Fiancé: The Other Way |
E927364
|
entity |
| Predicate | hasMultipleSeasons |
P197868
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [90 Day Fiancé: The Other Way, hasMultipleSeasons, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMultipleSeasons Context triple: [90 Day Fiancé: The Other Way, hasMultipleSeasons, true]
-
A.
numberOfSeasons
Indicates the total count of seasons associated with a particular entity (such as a series, competition, or event).
-
B.
hasEpisodes
Indicates that one entity (typically a series or show) contains or is composed of multiple episode entities.
-
C.
appearsInTelevisionSeasonCount
Indicates the number of television seasons in which the subject appears.
-
D.
isMultiSeasonParticipantOf
Indicates that an entity has participated in the related entity across multiple seasons or iterations.
-
E.
hasEpisodeCountInFirstSeries
Indicates that an entity has a specific number of episodes in its first series or season.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76eb5e034819088e53ab5b7909a68 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69feb342994081909481ec8ec5d44928 |
completed | May 9, 2026, 4:08 a.m. |
| PD | Predicate disambiguation | batch_69feb046e4e48190b96649aa28529cc9 |
completed | May 9, 2026, 3:55 a.m. |
| PDg | Predicate description generation | batch_69feb3419158819082f4666077535ca9 |
completed | May 9, 2026, 4:08 a.m. |
Created at: May 3, 2026, 4:16 p.m.